Dodge- Mci 2014- Slides

8/13/2019 Dodge- Mci 2014- Slides

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Biomarker Values Predictive of

Cognitive Decline in MCI:Baseline or Progression?

Hiroko H. Dodge, PhD

Associate Professo r of Neuro logy

Directo r, Bio stat ist ics and Data Core, Layton Ag ing

and Alzheimers Disease Center

Oregon Health and Science Universi ty

Portland , OR

Adjun ct Research Associate Professo r

Michigan Alzheimers Disease Center

An n, Arbo r, MI


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Nothing to disclose


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CHANGE POINT MODELS

Silbert, Dodge et. al. Trajectory of white matterhyperintensity burden preceding mild cognitive

impairment. Neurology 2012;79:741-747

(PMC3421153)

Buracchio, Dodge et. al. The trajectory of gait

speed preceding MCI. Archives of Neurology.

Archives of Neurology, 2010; 67:980-986

(PMC2921227)

Dodge, Ganguli,et al. Terminal decline and

practice effects in non-demented older adults.

Neurology, 2011:77;722-730. (PMC3164394)

Statistical Approaches: Examples


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APPLICATION OF MIXED

EFFECTS MODEL

Erten-Lyons, Dodge et. al.

Neuropathological basis of age-

associated brain atrophy. JAMA

Neurology 70:616-622, 2013.

Dodge, Ganguli et. al. Cohort

Effects in Age-Associated

Cognitive Trajectories. Journal of

Gerontology: Medical Sciences.

In press.



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LATENT TRAJECTORYANALYSIS

Dodge, Kayeet. al. In-

home walking speeds and

variability trajectories

associated with MildCognitive Impairment.

Neurology, 2012:

78(24):1946-1952.

(PMC3369505)



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Biomarkers vs. cognitive

outcomes

Cognitiv

eFunctions

time (age)

Individual Specific Deviations from population

mean trajectory (random components)

Variability explained by

Age, education, (reserve

factors)

Baseline biomarker values

Rate of

progression in

biomarker values

Population

mean/average

trajectory


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Aim/Data

To examine which components ---baselinevalues or biomarker progressions--

explains more variability in cognitive

declines in memory and executivefunctions.

DATA: theAlzheimers Disease

Neuroimaging Initiative (ADNI 1). 526subjects with valid data in at least one of

our variables of interest were used in this

study.


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Cognitive Outcomes

Trajectory (slope) of cognitive functions:1. ADNI-memory (ADNI-Mem) and

2. ADNI-executive (ADNI-Exe).

(Crane et al., 2012; Gibbons et al., 2012)

The scores are psychometrically optimized

composite scores of memory and executivefunction, derived from items from ADNI

neuropsychological tests


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Approach: 2-stage

Stage 1. Individual-specific slope of thelongitudinal trajectory of each biomarker

was estimated using mixed effects models.

Longitudinal trajectories of biomarkers model

= + + + + ,

where ( , ) ~ 0, and

~

(0,2).


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Approach

Variability in cognitive declines explained

by subject-specific baseline biomarker

valueswas compared with variability

explained by biomarker progressions.


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Results


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Outcome: memoryNormal Group

Biomarker

_bl: baseline values

_prog: progression

% of variability

explained by

biomarkers

Standardizedeffect size

ttau_bl (1 sd=51) -5.40% -0.01ttau_prog

(1 sd=0.21) -3.00% 0.02Abeta42_bl

(1 sd=56) -7.70% 0Abeta42_prog

(1 sd=0.14) -14.80% 0.02A positive percentage indicates that the corresponding predictor

explains the variation in cognitive decline, while a negative

percentage indicates that inclusion of the predictor adds more

estimation error instead of improving model fitting.


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Outcome: memory Normal GroupBiomarker


_prog: progression

% of variability

explained by

biomarkers

Standardized

effect sizeFDG-PET_bl

(1 sd=0.15) -0.60% 0.04FDG-PET_prog

(1 sd=0.18)

1.50%

0.1


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Outcome: memoryNormal Group

Biomarker

_bl: baseline

_prog: progression

% of variability

explained by

biomarkers

Standardized

effect sizePrecuneus

Thickness/icv_bl

(1 sd=2.3E-07) -2.36% 0.01PrecuneusThickness/icv_prog

(1 sd=0.10) -0.48% -0.01Medial Temporal

Thickn./icv_bl(1 sd=2.3E-07) -3.62% -0.01Medial Temporal

Thickn./icv_prog

(1 sd=0.10)-0.05% 0.1


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Outcome: memoryAmong MCI*

Biomarker


_prog: progression

% of variability

explained by

biomarkers


ttau_bl (1 sd=51) -1.90% 0ttau_prog

(1 sd=0.21) 0.30% -0.04Abeta42_bl

(1 sd=56) 5.10% -0.15Abeta42_prog

(1 sd=0.14) 10.30% -0.48FDG-PET_bl

(1 sd=0.15) 12.20% 0.09FDG-PET_prog

(1 sd=0.18) 12.70% 0.08


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Outcome: memory Among MCIBiomarker


_prog: progression

% of variability

explained by

biomarkers

Standardized effect

sizeLog_wmh/icv_bl

(1 sd=1.57) 0.50% -0.03Log_wmh/icv_prog

(1 sd=0.05) 0.10% 0.01Hpcv/icv_bl

(1 sd=3.7E-04) 9.00% 0.07Hpcv/icv_prog

( 1 sd=0.07) 19.80% 0.08Ventricles/icv_bl

(1 sd=5.8E-03) 8.70% -0.04Ventricles/icv_prog

(1 sd=0.11) 39.40% -0.12Total brain/icv_bl

(1 sd=0.04) 2.40% 0.06Total brain/icv_prog(1 sd=0.09) 16.00% 0.05


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Outcome: memory Among MCIBiomarker

_bl: baseline

_prog: progression

% of variability

explained by

biomarkers

Standardized

effect sizePrecuneus

Thickness/icv_bl

(1 sd=2.3E-07) 3.49% 0.09PrecuneusThickness/icv_prog

(1 sd=0.10) 5.38% 0.07Medial Temporal

Thickness/icv_bl(1 sd=2.3E-07) 4.36% 0.09Medial Temporal

Thickness/icv_prog

(1 sd=0.10)25.52% 0.11


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Outcome: memory Among ADBiomarker


_prog: progression

% of variability

explained by

biomarkers


ttau_bl (1 sd=51) -7.90% -0.19ttau_prog

(1 sd=0.21) -17.80% -0.08Abeta42_bl

(1 sd=56) -8.60% -0.05Abeta42_prog

(1 sd=0.14) 6.60% -0.04FDG-PET_bl

(1 sd=0.15) 30.00% 0.1FDG-PET_prog

(1 sd=0.18) 84.00% 0.12


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Outcome: memory Among ADBiomarker


_prog: progression

% of variability

explained by

biomarkers

Standardized effect

sizeLog_wmh/icv_bl

(1 sd=1.57) -4.70% 0.1Log_wmh/icv_prog

(1 sd=0.05) 3.00% 0.1Hpcv/icv_bl

(1 sd=3.7E-04) 4.70% -0.07Hpcv/icv_prog

( 1 sd=0.07) 26.00% 0.15Ventricles/icv_bl

(1 sd=5.8E-03)

4.20%

0.11

Ventricles/icv_prog

(1 sd=0.11) 63.80% -0.18Total brain/icv_bl

(1 sd=0.04) -4.50% 0.05Total brain/icv_prog(1 sd=0.09) 26.00% 0.17

O t


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Outcome: memoryAmong AD

Biomarker

_bl: baseline_prog: progression

% of

variability

explained bybiomarkers

Standardized

effect sizePrecuneus Thickness/icv_bl

(1 sd=2.3E-07) 5.14% 0.07PrecuneusThickness/icv_prog

(1 sd=0.10) 5.13% 0.08Medial Temporal

Thickness/icv_bl(1 sd=2.3E-07) 6.14% 0.09Medial Temporal

Thickness/icv_prog

(1 sd=0.10)64.65% 0.17


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Conclusions


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A number of studies have shown support for

the hypothetical AD progression model

developed by Jack et al (Jack et al., 2013;Jack et al., 2010).

Our study results also coincides with the

model; Across diagnostic groups, thepercentages of variability in cognitive declines

explained by functional (FDG-PET) or

structural (brain morphometric) biomarkers(either their baseline values or progressions)

increased significantly as disease progressed

from normal to AD.


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For most biomarkers, biomarker progressions

were more predictive of (associated with)memory decline than baseline values.

This suggests that clinical trials which require

recruiting at risk subjects could be improvedby using progression rather than baseline

values in biomarkers to enrich the study

subjects.

Future studies are warranted to estimate the

incremental effectiveness of improving clinical

trial statistical power by using biomarker

progression criteria


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Special Thanks to:

Michigan ADCRoger Albin, MD

Robert Koeppe, PhD

Oregon Health &

Science Univers i tyADC

Jeffrey Kaye, MD

Lisa Silbert, MD

UC DavisDaniel Harvey, PhD

Univers i ty o fPi t tsburgh

Mary Ganguli MD

Funding Sources

Michigan ADC pi lotgrant

R13 AG030995, FridayHarbor AdvancedPsychometric Work

Shop 2011

MCI Symposium

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Dodge- Mci 2014- Slides